diff --git a/docs/source/download_links.rst b/docs/source/download_links.rst
new file mode 100644
index 0000000000000000000000000000000000000000..7afc47338b7ca45ea92c6f4743db3d780edca254
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+++ b/docs/source/download_links.rst
@@ -0,0 +1,5 @@
+Download Links
+=====
+
+ - GitHub: https://github.com/GMAP/SPBench
+ - SPBench Website: https://gmap.pucrs.br/gmap/en/software/
diff --git a/docs/source/index.rst b/docs/source/index.rst
index b8badc09b13d7ec4aee3316c3e4ea2c1bacd5bf2..878343e6969955aa6a1aa02708ef88fab0522325 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -3,26 +3,29 @@ Welcome to SPBench's documentation!
 
 SPBench (Stream Processing Benchmark) is a framework for benchmarking C++ stream processing applications.
 The main goal of SPBench is to enable users to easily create custom benchmarks from real-world stream processing applications and evaluate multiple PPIs.
+
 The SPBench is intended for three main audiences:
 
-Users who want to run performance tests with the SPBench benchmarks. It implements different real-world stream processing applications using different parallel programming interfaces (PPIs) and parallelism patterns, and makes available the main metrics used in this domain. The framework also enables users to create custom benchmarks with new PPIs.
-Researchers and developers who want to test and evaluate new technologies and solutions. The SPBench benchmarks are also highly parameterizable and its API allows for easy and fast code reuse across all applications.
-Students and teachers who want to learn/teach stream parallelism. The benchmarks implemented with the SPBench API abstract the low-level code and expose to users only the stream core of each application (in a few lines of code). Therefore, it allows users to easily identify each individual operator and data dependencies, to understand what the stream flow looks like. Then users can build parallelism on top of this highly simplified code.
+   Users who want to run performance tests with the SPBench benchmarks. It implements different real-world stream processing applications using different parallel programming interfaces (PPIs) and parallelism patterns, and makes available the main metrics used in this domain. The framework also enables users to create custom benchmarks with new PPIs.
+
+   Researchers and developers who want to test and evaluate new technologies and solutions. The SPBench benchmarks are also highly parameterizable and its API allows for easy and fast code reuse across all applications.
+
+   Students and teachers who want to learn/teach stream parallelism. The benchmarks implemented with the SPBench API abstract the low-level code and expose to users only the stream core of each application (in a few lines of code). Therefore, it allows users to easily identify each individual operator and data dependencies, to understand what the stream flow looks like. Then users can build parallelism on top of this highly simplified code.
 
 The SPBench suite comprises the following stream processing applications and will be more in the future:
 
-   Ferret (PARSEC)
-   Lane Detection
-   Bzip2
-   Person Recognition (Temporarily removed due to license constraint)
+   - Ferret (PARSEC)
+   - Lane Detection
+   - Bzip2
+   - Person Recognition (Temporarily removed due to license constraint)
 
 For each of them there are parallel versions implemented using the following parallel programming interfaces and will be more in the future:
 
-   Intel TBB
-   FastFlow
-   SPar
-   GrPPI (forthcoming)
-   Standard C++ Threads (forthcoming)
+   - Intel TBB
+   - FastFlow
+   - SPar
+   - GrPPI (forthcoming)
+   - Standard C++ Threads (forthcoming)
 
 **Lumache** (/lu'make/) is a Python library for cooks and food lovers
 that creates recipes mixing random ingredients.
@@ -43,3 +46,4 @@ Contents
 
    usage
    api
+   download_links